Influences of species mixture on biomass of Masson pine (Pinus massoniana Lamb) forests
Bibliographic record
Abstract
The effect of tree diversity on productivity in subtropical forests in China is poorly understood. We investigated the biomasses of trees, understory vegetation, coarse roots, and fine roots with varying proportions of Pinus massoniana, mixed with other tree species in stands of the same age, to examine the effects of tree diversity. With an increase of P. massoniana proportion, the tree and understory biomasses increased at first, and then gradually decreased. As expected, the biomass of fine roots decreased with soil depth. Stands with 40 to 60% P. massoniana had the highest biomass, whereas stands with <20% P. massoniana had the least biomass. Stands with <20% P. massoniana had the least understory biomass, whereas those with 20 to 40% Masson pine had the least fine root biomass.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".